CKA-based pre-fine-tuning layer pruning selects redundant ViT depth on unlabeled EO task data, cutting up to ~79% parameters while retaining most task performance and speeding both train and inference.
Neural Networks153, 461–473 (2022)
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SIMPLER: Efficient Foundation Model Adaptation via Similarity-Guided Layer Pruning for Earth Observation
CKA-based pre-fine-tuning layer pruning selects redundant ViT depth on unlabeled EO task data, cutting up to ~79% parameters while retaining most task performance and speeding both train and inference.